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          <h1 class="title is-1 publication-title">Video-Text as Game Players:<br /> Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning</h1>
          <div class="is-size-5 publication-authors">
            <span class="author-block">Peng Jin<sup>1,3</sup>, </span>
            <span class="author-block">Jinfa Huang<sup>1,3</sup>, </span>
            <span class="author-block">Pengfei Xiong<sup>4</sup>, </span>
            <span class="author-block">Shangxuan Tian<sup>4</sup>, </span> <br />
            <span class="author-block">Chang Liu<sup>5</sup>, </span>
            <span class="author-block">Xiangyang Ji<sup>5</sup>, </span>
            <span class="author-block">Li Yuan<sup>1,2,3 *</sup>, </span>
            <span class="author-block">Jie Chen<sup>1,2,3 *</sup>, </span>
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          <div class="is-size-8 publication-authors">
            <span class="author-block"><sup>1</sup>School of Electronic and Computer Engineering, Peking University </span>
            <span class="author-block"><sup>2</sup>Peng Cheng Laboratory </span> <br />
            <span class="author-block"><sup>3</sup>AI for Science (AI4S)-Preferred Program, Peking University </span> <br />
            <span class="author-block"><sup>4</sup>Shopee </span>
            <span class="author-block"><sup>5</sup>Department of Automation and BNRist, Tsinghua University </span>
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        <h2 class="title is-3">Abstract</h2>
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          <p>
            Contrastive learning-based video-language representation learning approaches,
            e.g., CLIP, have achieved outstanding performance, which pursue semantic interaction
            upon pre-defined video-text pairs. To clarify this coarse-grained global interaction
            and move a step further, we have to encounter challenging shell-breaking interactions
            for fine-grained cross-modal learning.
            In this paper, we creatively model video-text as
            game players with multivariate cooperative game theory to wisely handle the uncertainty
            during fine-grained semantic interaction with diverse granularity, flexible combination,
            and vague intensity. Concretely, we propose Hierarchical Banzhaf
            Interaction (HBI) to value possible correspondence between video frames and
            text words for sensitive and explainable cross-modal contrast. To efficiently realize the
            cooperative game of multiple video frames and multiple text words, the proposed method
            clusters the original video frames (text words) and computes the Banzhaf Interaction
            between the merged tokens. By stacking token merge modules, we achieve cooperative games
            at different semantic levels.
            Extensive experiments on commonly used text-video retrieval and video-question answering
            benchmarks with superior performances justify the efficacy of our HBI. More encouragingly,
            it can also serve as a visualization tool to promote the understanding of cross-modal
            interaction, which have a far-reaching impact on the community.
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        <h2 class="title is-3">Visualization</h2>

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            <p>Example 1</p>
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            <p>Example 2</p>
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            <p>Example 3</p>
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            <p>Example 4</p>
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            <p>Example 5</p>
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            <p>Example 6</p>
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            <img src="./static/images/Visualization_0.png"
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            <p>Example 7</p>
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